Publication

Learning spatio-temporal patterns in the presence of input noise using phase-change memristors

Publications associées (32)

Graph Chatbot

Chattez avec Graph Search

Posez n’importe quelle question sur les cours, conférences, exercices, recherches, actualités, etc. de l’EPFL ou essayez les exemples de questions ci-dessous.

AVERTISSEMENT : Le chatbot Graph n'est pas programmé pour fournir des réponses explicites ou catégoriques à vos questions. Il transforme plutôt vos questions en demandes API qui sont distribuées aux différents services informatiques officiellement administrés par l'EPFL. Son but est uniquement de collecter et de recommander des références pertinentes à des contenus que vous pouvez explorer pour vous aider à répondre à vos questions.

Connectez-vous pour utiliser Chat avec Graph Search

Deep Learning Theory Through the Lens of Diagonal Linear Networks

Scott William Pesme

In this PhD manuscript, we explore optimisation phenomena which occur in complex neural networks through the lens of

2

-layer diagonal linear networks. This rudimentary architecture, which consists of a two layer feedforward linear network with a diagonal ...

EPFL2024

Learning spatio-temporal patterns in the presence of input noise using phase-change memristors

Graph Chatbot

Chattez avec Graph Search

Fear learning induces synaptic potentiation between engram neurons in the rat lateral amygdala

Predicting Visual Stimuli From Cortical Response Recorded With Wide-Field Imaging in a Mouse

Deep Learning Theory Through the Lens of Diagonal Linear Networks

Random matrix methods for high-dimensional machine learning models

Task-driven neural network models predict neural dynamics of proprioception: Neural network model weights

Predicting the long-term collective behaviour of fish pairs with deep learning

Infusing structured knowledge priors in neural models for sample-efficient symbolic reasoning

Exploring High-Performance and Energy-Efficient Architectures for Edge AI-Enabled Applications

Aggregating Spatial and Photometric Context for Photometric Stereo

Fast adaptation to rule switching using neuronal surprise

Predicting Visual Stimuli From Cortical Response Recorded With Wide-Field Imaging in a Mouse

Deep Learning Theory Through the Lens of Diagonal Linear Networks

Fear learning induces synaptic potentiation between engram neurons in the rat lateral amygdala

Task-driven neural network models predict neural dynamics of proprioception: Neural network model weights

Infusing structured knowledge priors in neural models for sample-efficient symbolic reasoning

Exploring High-Performance and Energy-Efficient Architectures for Edge AI-Enabled Applications

Random matrix methods for high-dimensional machine learning models

Predicting the long-term collective behaviour of fish pairs with deep learning

Aggregating Spatial and Photometric Context for Photometric Stereo

Fast adaptation to rule switching using neuronal surprise